Author: NEST CoreDev

# Using regression plots

#### NEST CoreDev

# `teal` application to use regression plot with various datasets types

This vignette will guide you through the four parts to create a `teal` application using various types of datasets using the regression plot module `tm_a_regression()`:

1. Load libraries
2. Create data sets
3. Create an `app` variable
4. Run the app

## 1 - Load libraries

    library(teal.modules.general) # used to create the app
    library(dplyr) # used to modify data sets

## 2 - Create data sets

Inside this app 4 datasets will be used

1. `ADSL` A wide data set with subject data
2. `ADRS` A long data set with response data for subjects at different time points of the study
3. `ADTTE` A long data set with time to event data
4. `ADLB` A long data set with lab measurements for each subject

    data <- teal_data()
    data <- within(data, {
      ADSL <- teal.data::rADSL %>%
        mutate(TRTDUR = round(as.numeric(TRTEDTM - TRTSDTM), 1))
      ADRS <- teal.data::rADRS
      ADTTE <- teal.data::rADTTE
      ADLB <- teal.data::rADLB %>%
        mutate(CHGC = as.factor(case_when(
          CHG < 1 ~ "N",
          CHG > 1 ~ "P",
          TRUE ~ "-"
        )))
    })
    join_keys(data) <- default_cdisc_join_keys[names(data)]

## 3 - Create an `app` variable

This is the most important section. We will use the `teal::init()` function to create an app. The data will be handed over using `teal.data::teal_data()`. The app itself will be constructed by multiple calls of `tm_a_regression()` using different combinations of data sets.

    # configuration for the single wide dataset
    mod1 <- tm_a_regression(
      label = "Single wide dataset",
      response = data_extract_spec(
        dataname = "ADSL",
        select = select_spec(
          label = "Select variable:",
          choices = variable_choices(data[["ADSL"]], c("BMRKR1", "BMRKR2")),
          selected = "BMRKR1",
          multiple = FALSE,
          fixed = FALSE
        )
      ),
      regressor = data_extract_spec(
        dataname = "ADSL",
        select = select_spec(
          label = "Select variables:",
          choices = variable_choices(data[["ADSL"]], c("AGE", "SEX", "RACE")),
          selected = "AGE",
          multiple = TRUE,
          fixed = FALSE
        )
      )
    )

    # configuration for the two wide datasets
    mod2 <- tm_a_regression(
      label = "Two wide datasets",
      default_plot_type = 2,
      response = data_extract_spec(
        dataname = "ADSL",
        select = select_spec(
          label = "Select variable:",
          choices = variable_choices(data[["ADSL"]], c("BMRKR1", "BMRKR2")),
          selected = "BMRKR1",
          multiple = FALSE,
          fixed = FALSE
        )
      ),
      regressor = data_extract_spec(
        dataname = "ADSL",
        select = select_spec(
          label = "Select variables:",
          choices = variable_choices(data[["ADSL"]], c("AGE", "SEX", "RACE")),
          selected = c("AGE", "RACE"),
          multiple = TRUE,
          fixed = FALSE
        )
      )
    )

    # configuration for the same long datasets (same subset)
    mod3 <- tm_a_regression(
      label = "Same long datasets (same subset)",
      default_plot_type = 2,
      response = data_extract_spec(
        dataname = "ADTTE",
        select = select_spec(
          label = "Select variable:",
          choices = variable_choices(data[["ADTTE"]], c("AVAL", "CNSR")),
          selected = "AVAL",
          multiple = FALSE,
          fixed = FALSE
        ),
        filter = filter_spec(
          label = "Select parameter:",
          vars = "PARAMCD",
          choices = value_choices(data[["ADTTE"]], "PARAMCD", "PARAM"),
          selected = "PFS",
          multiple = FALSE
        )
      ),
      regressor = data_extract_spec(
        dataname = "ADTTE",
        select = select_spec(
          label = "Select variable:",
          choices = variable_choices(data[["ADTTE"]], c("AGE", "CNSR", "SEX")),
          selected = c("AGE", "CNSR", "SEX"),
          multiple = TRUE
        ),
        filter = filter_spec(
          label = "Select parameter:",
          vars = "PARAMCD",
          choices = value_choices(data[["ADTTE"]], "PARAMCD", "PARAM"),
          selected = "PFS",
          multiple = FALSE
        )
      )
    )

    # configuration for the wide and long datasets
    mod4 <- tm_a_regression(
      label = "Wide and long datasets",
      response = data_extract_spec(
        dataname = "ADLB",
        filter = list(
          filter_spec(
            vars = "PARAMCD",
            choices = value_choices(data[["ADLB"]], "PARAMCD", "PARAM"),
            selected = levels(data[["ADLB"]]$PARAMCD)[2],
            multiple = TRUE,
            label = "Select measurement:"
          ),
          filter_spec(
            vars = "AVISIT",
            choices = levels(data[["ADLB"]]$AVISIT),
            selected = levels(data[["ADLB"]]$AVISIT)[2],
            multiple = TRUE,
            label = "Select visit:"
          )
        ),
        select = select_spec(
          label = "Select variable:",
          choices = "AVAL",
          selected = "AVAL",
          multiple = FALSE,
          fixed = TRUE
        )
      ),
      regressor = data_extract_spec(
        dataname = "ADSL",
        select = select_spec(
          label = "Select variables:",
          choices = variable_choices(data[["ADSL"]], c("BMRKR1", "BMRKR2", "AGE")),
          selected = "AGE",
          multiple = TRUE,
          fixed = FALSE
        )
      )
    )

    # configuration for the same long datasets (different subsets)
    mod5 <- tm_a_regression(
      label = "Same long datasets (different subsets)",
      default_plot_type = 2,
      response = data_extract_spec(
        dataname = "ADLB",
        filter = list(
          filter_spec(
            vars = "PARAMCD",
            choices = value_choices(data[["ADLB"]], "PARAMCD", "PARAM"),
            selected = levels(data[["ADLB"]]$PARAMCD)[1],
            multiple = TRUE,
            label = "Select lab:"
          ),
          filter_spec(
            vars = "AVISIT",
            choices = levels(data[["ADLB"]]$AVISIT),
            selected = levels(data[["ADLB"]]$AVISIT)[1],
            multiple = TRUE,
            label = "Select visit:"
          )
        ),
        select = select_spec(
          choices = "AVAL",
          selected = "AVAL",
          multiple = FALSE,
          fixed = TRUE
        )
      ),
      regressor = data_extract_spec(
        dataname = "ADLB",
        filter = list(
          filter_spec(
            vars = "PARAMCD",
            choices = value_choices(data[["ADLB"]], "PARAMCD", "PARAM"),
            selected = levels(data[["ADLB"]]$PARAMCD)[1],
            multiple = FALSE,
            label = "Select labs:"
          ),
          filter_spec(
            vars = "AVISIT",
            choices = levels(data[["ADLB"]]$AVISIT),
            selected = levels(data[["ADLB"]]$AVISIT)[1],
            multiple = FALSE,
            label = "Select visit:"
          )
        ),
        select = select_spec(
          choices = variable_choices(data[["ADLB"]], c("AVAL", "AGE", "BMRKR1", "BMRKR2", "SEX", "ARM")),
          selected = c("AVAL", "BMRKR1"),
          multiple = TRUE
        )
      )
    )

    # initialize the app
    app <- init(
      data = data,
      modules = modules(
        modules(
          label = "Regression plots",
          mod1,
          mod2,
          mod3,
          mod4,
          mod5
        )
      )
    )

## 4 - Run the app

A simple `shiny::shinyApp()` call will let you run the app. Note that app is only displayed when running this code inside an `R` session.

    shinyApp(app$ui, app$server, options = list(height = 1024, width = 1024))

## 5 - Try it out in Shinylive

Open in Shinylive